It is of major interest to industry to eliminate the use of coolants wherever possible in the manufacturing process, both for environmental and cost reasons. "Cutting dry" is best performed in most cases with ceramic, CBN and other cutting tools which desirably operate at elevated temperatures. However these tools are often brittle, in tolerant of mechanical or thermal shock, and unpredictable in their Time to failure in terms of parts cut, which makes them able to benefit from the adaptive control methods described herein.
It is also desirable where economically and technically feasible to add higher degrees of adaptive control to machining processes, such that benefits of increased quality and higher productivity can be obtained. Use of such adaptive control can greatly improve the use of machining processes to turn precision finishes and diameters in normal production processes, and facilitate finishing and grooving, using cutting tools, as opposed to grinding in hardened materials.
Adaptive control can also reduce set up time and the engineering time to integrate new processes.
This application concerns adaptive control aimed at solution of the above problems and others. Particularly considered are the high production processes, where such adaptive control techniques can optimize production in largely automated plants, where human intervention is often not possible. The key to all of these issues, is increased sensory capabilities in the machine, and the requisite software algorithms, etc. to deal with the sensed data.
To summarize:
Goals in part are to
Facilitate coolant free operation. PA2 Reduce part variation in production. PA2 Improve machine accuracy. PA2 Increase tool life, and reduce effects of tool breakage. PA2 Increase machining rates via speed and feed optimization. PA2 Reduce scrap and improve stock utilization. PA2 Reduce downtime due to Tool adjustments, dimension verification, tool breakage and wear, machine breakdowns and crashes. PA2 Provide feedback to previous operations.
Despite the important potential of adaptive control of machining there has been very little actual implementation on the plant floor of such technologies. Even in the research community, the vast majority of research papers presented have all been concerned with force based variables in milling, turning, and grinding, which infer a condition being generated on the part, with very little attempt to actually relate to actual part or tool monitoring technologies; and their integration with the intelligence of the machining process.
The reason for this lack of attention is unknown, but probably relates to the difficulty in sensing of many of variables, particularly when the machining is wet, which precludes (or gave to many the impression of precluding) the otherwise very desirable non-contact sensors, particularly electro-optical.